Computer-implemented calculation of corn harvest recommendations

ABSTRACT

A computer system and computer-implemented techniques for determining crop harvest times during a growing season based upon hybrid seed properties, weather conditions, and geo-location of planted fields is provided. In an embodiment, determining crop harvest times for corn fields may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including seed type and relative maturity, and weather data for the specific geo-location of the agricultural field. Weather data includes temperature, humidity, and dew point for a specified period of days. Using digitally programmed equilibrium moisture content logic within the computer system to create and store, in computer memory, an equilibrium moisture content time series for the specific geo-location that is based upon weather data. The equilibrium moisture content is used to determine the rate of grain dry down because it gives a basis for how strongly water vapor will dissipate from a kernel to open air. Using digitally programmed grain moisture logic of the computer system to calculate and store in computer memory R6 moisture content for a specific hybrid seed based on a plurality of hybrid seed data. Using digitally programmed grain dry down logic of the computer system to create and store in computer memory a grain dry down time series model for the specific hybrid seed at the specific geo-location that represents the estimated moisture content of the kernel over specified time data points. The grain dry down time series is based upon the equilibrium moisture content time series, the estimated R6 date, the estimated R6 moisture content value, and specific hybrid seed properties. Using digitally programmed harvest recommendation logic of the computer system to determine and display a harvest time recommendation for harvesting crop grown from a specific hybrid seed plant based on the grain dry down time series and the desired moisture level of the grower.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/813,509, filed Mar. 9, 2020, which is a continuation of U.S.application Ser. No. 14/925,797, filed Oct. 28, 2015, now U.S. Pat. No.10,586,158, the entire contents of each of which is incorporated hereinby reference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to computer-implemented techniques formodeling grain moisture related to determining optimal harvest time forhybrid corn seeds based upon seed type, agricultural field data, andweather data.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Harvested corn grain is classified into five grades, with the highestquality grade, Grade No. 1, being the most expensive. Classifying corngrain involves classifying a minimum weight per bushel and a percentageof damaged kernels per bushel. Although grain moisture is not used indetermining corn grain quality grades, it is used to determine a salesprice per bushel of a particular grade. Grain moisture refers to and ismeasured as the ratio of water mass to wet kernel mass, referred toherein as “wet-basis”. Grain moisture level is important to buyersbecause the level of moisture in grain can affect the amount ofdegradation of grain during storage and shipment. Therefore buyersgenerally request that grain moisture be around 15.5% or less. If agrower harvested corn that has higher than desired grain moisture, thenbuyers may demand a discount for the harvested corn. The cost may besignificant.

For example, the economic impact of a grower who harvested 1000 lbs. ofcorn with grain moisture of 20% is that the price per bushel would bediscounted based upon the harvest weight after drying down the harvestto the desired moisture level. In this case, drying down 1000 lbs. ofharvest at a moisture level of 20% to a moisture level of 15.5% wouldshrink the overall harvest weight to approximately 955 lbs. This loss intotal weight would equate to about a 4.5% write-down in value. Thereforethe economic impact can be significant if a harvest is not at thedesired grain moisture level.

As a kernel grows, the corn plant transfers water and nutrients to thekernel. During maturation the amount of kernel moisture begins to slowlydecrease. When the kernel reaches physiological maturity, referred to asR6 stage, there is a passive exchange of moisture between the kernel andoutside air. The R6 stage is also referred to as “black layer” becausephysiological maturity occurs when a black layer forms at the base ofthe kernels. The black layer is a hard starch layer that turns black orbrown and cuts off the water and dry matter transfer to the kernel. Oncethe R6 stage is reached the decrease in kernel moisture is primarily dueto the rate of water loss from the kernel itself to outside air. Thisrate is referred to as grain dry down.

Grain dry down is influenced by many factors. One such factor is theambient air temperature and humidity. Higher humidity or coolertemperatures may slow grain dry down because there is less differencebetween the humidity in the kernel and the ambient air. Conversely,areas where the humidity and/or temperature are low the grain dry downmay be accelerated. Therefore growers must account for current andfuture weather conditions when estimating the ideal harvest time basedon grain dry down.

Producers of hybrid corn seeds provide relative maturity ratings to helpgrowers predict when to harvest their grain based upon the environmentand the type of hybrid seed. Relative maturity is a method to classify acorn hybrid seed based on the mega-environment where it is planted.Relative maturity is a rating system that helps determine when a hybridmay be safely harvested with minimal harvest loss due to excessivemoisture or kernel damage, usually based upon the assumption that grainmoisture loss equals about 0.5 percentage points per day. Therefore twodays of field drying equals one day of relative maturity. For example,if hybrid A is assigned a relative maturity of 110 and hybrid B isassigned a relative maturity of 114, and if hybrid A and hybrid B areplanted on the same day, then it is understood that on average hybrid Bhas two percentage points more moisture than hybrid A when hybrid Acompletes its dry down. Growers use the relative maturity data toapproximate when to harvest their corn based upon relative maturityvalues.

Another factor in determining ideal harvest times is determining when R6begins, as the physical black layer at the base of the kernel is notvisible unless the corn is dissected. Therefore growers estimate thebeginning of the R6 stage based upon approximations provided byproducers and historical observed data of the different hybrid seeds.However, knowing the rate of grain dry down and the approximate startdate of R6 is only helpful if growers know the initial grain moisturecontent at the beginning of R6. Producers and growers approximateaverage moisture for corn hybrids at about 30%. By using generalizedgrain moisture content at R6, dry down predictions are prone to error ifthe starting moisture content is not near the estimated 30%. Individualfluctuations between grain moisture content at R6 for specific hybridseed varieties may lead to errors when predicting harvest times basedupon a target grain moisture content at harvest.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2A, FIG. 2B illustrate two views of an example logical organizationof sets of instructions in main memory when an example mobileapplication is loaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system 400 uponwhich an embodiment of the invention may be implemented.

FIG. 5 depicts an example programmed algorithm or process fordetermining optimal harvest time for a specific corn planted at aspecific geo-location based upon target grain moisture content atharvest.

FIG. 6 depicts an example programmed algorithm or process for receivingagricultural data from external sources.

FIG. 7 depicts an example programmed algorithm or process by which grainmoisture logic is used to estimate grain moisture content for thespecific hybrid seed at R6.

FIG. 8 depicts an example programmed algorithm or process by which graindry down logic is used to calculate daily dry down rates starting at R6.

FIG. 9 depicts a graphical representation of a sample harvestrecommendation model that may be displayed digital form on a computerdisplay.

FIG. 10 depicts an example embodiment of a timeline view for data entry.

FIG. 11 depicts an example embodiment of a spreadsheet view for dataentry.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. SOIL PROPERTY ESTIMATION SUBSYSTEM    -   2.6. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. FUNCTIONAL OVERVIEW

-   -   3.1. RECEIVING DATA    -   3.2. EQUILIBRIUM MOISTURE CONTENT LOGIC    -   3.3. GRAIN MOISTURE LOGIC    -   3.4. GRAIN DRY DOWN LOGIC    -   3.5. HARVEST RECOMMENDATION LOGIC

1. General Overview

A computer system and computer-implemented techniques are provided fordetermining crop harvest times during a growing season based upon hybridseed properties, weather conditions, and geo-location of planted fields.In an embodiment, determining crop harvest times for corn fields may beaccomplished using a server computer system that is programmed toreceive, over a digital communication network, electronic digital datarepresenting hybrid seed properties, including seed type and relativematurity, and weather data for the specific geo-location of theagricultural field. Weather data includes temperature, humidity, and dewpoint for a specified period of days. Using digitally programmedequilibrium moisture content logic within the computer system, thesystem is programmed to create and store, in computer memory, anequilibrium moisture content time series for the specific geo-locationthat is based upon weather data. Equilibrium moisture content on aparticular day represents the expected dry-basis equilibrium moisturecontent fraction that would be found in the kernel at the specificgeo-location if an unlimited amount of time is allowed for moisture inthe kernel and in air to exchange and reach equilibrium according to theweather condition of that particular day. The equilibrium moisturecontent is used to determine the rate of grain dry down based uponcomputer-implemented calculations of how strongly water vapor willdissipate from a kernel to open air for a specific day.

Using digitally programmed grain moisture logic, the computer system isprogrammed to calculate and store in computer memory R6 moisture contentfor a specific hybrid seed based on a plurality of hybrid seed data. TheR6 moisture content is important for determining the estimated startingmoisture of the kernels for the grain dry down time series model that isdiscussed next.

Using digitally programmed grain dry down logic, the computer systemalso is programmed to create and store in computer memory a grain drydown time series model for the specific hybrid seed at the specificgeo-location that represents the estimated moisture content of thekernel over specified time data points. “Model,” in this context, refersto a set of computer executable instructions and associated data thatcan be invoked, called, executed, resolved or calculated to yielddigitally stored output data based upon input data that is received inelectronic digital form. It is convenient, at times, in this disclosureto specify a model using one or more mathematical equations, but anysuch model is intended to be implemented in programmedcomputer-executable instructions that are stored in memory withassociated data. The grain dry down time series is based upon theequilibrium moisture content time series, the estimated R6 date, theestimated R6 moisture content, and specific hybrid seed properties.

Using digitally programmed harvest recommendation logic, the computersystem is programmed to determine and display a harvest timerecommendation for harvesting crop grown from a specific hybrid seedplant based on the grain dry down time series and the desired moisturelevel of the grower.

2. Example Agricultural Intelligence Computer System 2.1 StructuralOverview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant, application date, amount, source,method), (g) irrigation data (for example, application date, amount,source, method), (h) weather data (for example, precipitation,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

An data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 has one or more remote sensors 112 fixedthereon, which sensors are communicatively coupled either directly orindirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a colorgraphical screen display that is mounted within an operator's cab of theapparatus 111. Cab computer 115 may implement some or all of theoperations and functions that are described further herein for themobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 10 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 10, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline include Nitrogen,Planting, Practices, and Soil. To add a nitrogen application event, auser computer may provide input to select the nitrogen tab. The usercomputer may then select a location on the timeline for a particularfield in order to indicate an application of nitrogen on the selectedfield. In response to receiving a selection of a location on thetimeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 10, the top two timelines have the “Fall applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 10,if the “Fall applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 10, the interface may update to indicatethat the “Fall applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Fall applied” program would not alter the April application ofnitrogen.

FIG. 11 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 11, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 11. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 11 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel data may include a model of past events on the one or more fields,a model of the current status of the one or more fields, and/or a modelof predicted events on the one or more fields. Model and field data maybe stored in data structures in memory, rows in a database table, inflat files or spreadsheets, or other forms of stored digital data.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2A, FIG. 2B illustrate two views of an example logical organizationof sets of instructions in main memory when an example mobileapplication is loaded for execution. In FIG. 2A, FIG. 2B, each namedelement represents a region of one or more pages of RAM or other mainmemory, or one or more blocks of disk storage or other non-volatilestorage, and the programmed instructions within those regions. In oneembodiment, in FIG. 2A, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202, overview andalert instructions 204, digital map book instructions 206, seeds andplanting instructions 208, nitrogen instructions 210, weatherinstructions 212, field health instructions 214, and performanceinstructions 216.

In one embodiment, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202 which areprogrammed to receive, translate, and ingest field data from third partysystems via manual upload or APIs. Data types may include fieldboundaries, yield maps, as-planted maps, soil test results, as-appliedmaps, and/or management zones, among others. Data formats may includeshape files, native data formats of third parties, and/or farmmanagement information system (FMIS) exports, among others. Receivingdata may occur via manual upload, e-mail with attachment, external APIsthat push data to the mobile application, or instructions that call APIsof external systems to pull data into the mobile application. In oneembodiment, mobile computer application 200 comprises a data inbox. Inresponse to receiving a selection of the data inbox, the mobile computerapplication 200 may display a graphical user interface for manuallyuploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into soil zones along with a panel identifying eachsoil zone and a soil name, texture, and drainage for each zone. Mobilecomputer application 200 may also display tools for editing or creatingsuch, such as graphical tools for drawing soil zones over a map of oneor more fields. Planting procedures may be applied to all soil zones ordifferent planting procedures may be applied to different subsets ofsoil zones. When a script is created, mobile computer application 200may make the script available for download in a format readable by anapplication controller, such as an archived or compressed format.Additionally and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use. In one embodiment,nitrogen instructions 210 are programmed to provide tools to informnitrogen decisions by visualizing the availability of nitrogen to crops.This enables growers to maximize yield or return on investment throughoptimized nitrogen application during the season. Example programmedfunctions include displaying images such as SSURGO images to enabledrawing of application zones and/or images generated from subfield soildata, such as data obtained from sensors, at a high spatial resolution(as fine as 10 meters or smaller because of their proximity to thesoil); upload of existing grower-defined zones; providing an applicationgraph and/or a map to enable tuning application(s) of nitrogen acrossmultiple zones; output of scripts to drive machinery; tools for massdata entry and adjustment; and/or maps for data visualization, amongothers. “Mass data entry,” in this context, may mean entering data onceand then applying the same data to multiple fields that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields of the same grower, but such massdata entry applies to the entry of any type of field data into themobile computer application 200. For example, nitrogen instructions 210may be programmed to accept definitions of nitrogen planting andpractices programs and to accept user input specifying to apply thoseprograms across multiple fields. “Nitrogen planting programs,” in thiscontext, refers to a stored, named set of data that associates: a name,color code or other identifier, one or more dates of application, typesof material or product for each of the dates and amounts, method ofapplication or incorporation such as injected or knifed in, and/oramounts or rates of application for each of the dates, crop or hybridthat is the subject of the application, among others. “Nitrogenpractices programs,” in this context, refers to a stored, named set ofdata that associates: a practices name; a previous crop; a tillagesystem; a date of primarily tillage; one or more previous tillagesystems that were used; one or more indicators of application type, suchas manure, that were used. Nitrogen instructions 210 also may beprogrammed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium) application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, hybrid, population, SSURGO, soil tests, orelevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to FIG. 2B, in one embodiment acab computer application 220 may comprise maps-cab instructions 222,remote view instructions 224, data collect and transfer instructions226, machine alerts instructions 228, script transfer instructions 230,and scouting-cab instructions 232. The code base for the instructions ofFIG. 2B may be the same as for FIG. 2A and executables implementing thecode may be programmed to detect the type of platform on which they areexecuting and to expose, through a graphical user interface, only thosefunctions that are appropriate to a cab platform or full platform. Thisapproach enables the system to recognize the distinctly different userexperience that is appropriate for an in-cab environment and thedifferent technology environment of the cab. The maps-cab instructions222 may be programmed to provide map views of fields, farms or regionsthat are useful in directing machine operation. The remote viewinstructions 224 may be programmed to turn on, manage, and provide viewsof machine activity in real-time or near real-time to other computingdevices connected to the system 130 via wireless networks, wiredconnectors or adapters, and the like. The data collect and transferinstructions 226 may be programmed to turn on, manage, and providetransfer of data collected at machine sensors and controllers to thesystem 130 via wireless networks, wired connectors or adapters, and thelike. The machine alerts instructions 228 may be programmed to detectissues with operations of the machine or tools that are associated withthe cab and generate operator alerts. The script transfer instructions230 may be configured to transfer in scripts of instructions that areconfigured to direct machine operations or the collection of data. Thescouting-cab instructions 230 may be programmed to displaylocation-based alerts and information received from the system 130 basedon the location of the agricultural apparatus 111 or sensors 112 in thefield and ingest, manage, and provide transfer of location-basedscouting observations to the system 130 based on the location of theagricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

2.4 Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge at the samelocation or an estimate of nitrogen content with a soil samplemeasurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise and distorting effects within the agronomicdata including measured outliers that would bias received field datavalues. Embodiments of agronomic data preprocessing may include, but arenot limited to, removing data values commonly associated with outlierdata values, specific measured data points that are known tounnecessarily skew other data values, data smoothing techniques used toremove or reduce additive or multiplicative effects from noise, andother filtering or data derivation techniques used to provide cleardistinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5 Harvest Time Estimation Subsystem

In an embodiment, the agricultural intelligence computer system 130,among other components, includes a harvest time estimation subsystem170. The harvest time estimation subsystem 170 is configured to providea harvest time recommendation for harvesting planted crop usingagricultural data values from one or more sources. The harvest timeestimation subsystem 170 uses field data 106 and external data 110 tocreate digital models of grain moisture dry down rates for specifichybrid seeds of corn.

In an embodiment, the harvest time estimation subsystem 170 containsspecially configured logic including, but not limited to, equilibriummoisture content logic 172, grain moisture logic 173, grain dry downlogic 174, harvest recommendation logic 175, and harvest timeapplication logic 171. Each of the foregoing elements is furtherdescribed in structure and function in other sections herein. “Logic,”as used in 1, refers in at least one embodiment to regions of mainmemory in the agricultural intelligence computer system 130 into whichprogrammed, executable instructions have been loaded, and whichinstructions are configured when executed to cause the computer toperform the functions that are described herein for that logicalelement. For example, equilibrium moisture content logic 172 indicates aregion of main memory into which the computer has loaded instructions,which when executed cause performing the interface functions that arefurther described herein. These elements of FIG. 1 also indirectlyindicate how a typical programmer or software engineer would organizethe source code of programs that implement the functions that aredescribed; the code may be organized into logical modules, methods,subroutines, branches, or other units using an architecturecorresponding to FIG. 1.

In an embodiment, the equilibrium moisture content logic 172 isgenerally configured or programmed to construct equilibrium moisturecontent (EMC) time series based upon hybrid seed properties and dailyweather data for a specific geo-location. An EMC time series is acollection of daily EMC values, where an EMC value represents themoisture content that a specific grain will eventually reach if the EMCvalue is held constant. EMC value is a function of temperature andrelative humidity of ambient air. The grain moisture logic 173 isgenerally configured or programmed to calculate the grain moisturecontent of a specific hybrid seed at the start of the black layer (atkernel maturity R6) based upon observed and estimated hybrid seedproperty data of multiple hybrid seed varieties. The grain dry downlogic 174 is generally configured or programmed to construct a grain drydown time series based upon the EMC time series, the grain moisturecontent of a specific hybrid seed at R6, relative maturity of a specifichybrid seed, and calculated drying coefficients based upon historicaldata of hybrid seed varieties. The harvest recommendation logic 175 isgenerally configured or programmed to evaluate the grain dry down timeseries and calculate the optimal harvest date.

Each of harvest time application logic 171, EMC logic 172, grainmoisture logic 173, grain dry down logic 174, and harvest recommendationlogic 175 may be implemented using one or more computer programs orother software elements that are loaded into and executed using one ormore general-purpose computers, logic implemented in field programmablegate arrays (FPGAs) or application-specific integrated circuits (ASICs).While FIG. 1 depicts harvest time application logic 171, grain dry downlogic 174, EMC logic 172, grain moisture logic 173, and harvestrecommendation logic 175 in one computing system, in variousembodiments, logics 171, 172, 173, 174, and 175 may operate on multiplecomputing systems.

In an embodiment, the implementation of the functions described hereinfor harvet time application logic 171, EMC logic 172, grain moisturelogic 173, grain dry down logic 174, and harvest recommendation logic175 using one or more computer programs or other software elements thatare loaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Each of the items of logic in FIG. 1,and in all other drawing figures herein, may represent a region or setof one or more pages of main memory storing programmed instructionswhich when executed cause performing the process steps or algorithmsteps that are disclosed herein. Thus the logic elements do notrepresent mere abstractions but represent real pages of memory that havebeen loaded with executable instructions. Further, each of the flowdiagrams that are described further herein may serve as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, external data server computer 108 stores external data110, including historical grain moisture for a variety of hybrid seedsand weather data representing daily temperatures and humidity on one ormore fields. Historical grain moisture may include, but is not limitedto, estimated R6 dates for hybrid seed varieties, relative maturity forhybrid seed varieties, observed grain moisture at harvest, andgeo-location specific data for each hybrid seed variety recorded. Theweather data may include past and present daily temperatures includinghighs, lows, and dew point temperatures. In an embodiment, external dataserver 108 comprises a plurality of servers hosted by differententities. For example, a first server may contain hybrid seed propertydata while a second server may include weather data.

2.6 Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3.0 Functional Overview

FIG. 5 is a flow diagram that depicts a process for determining optimalharvest time for a specific hybrid seed of corn planted at a specificgeo-location based upon target grain moisture content at harvest. FIG. 5may be implemented, in one embodiment, by programming the elements ofthe agricultural intelligence computer system 130 to perform thefunctions that are described in this section, which may representdisclosure of an algorithm for computer implementation of the functionsthat are described. For purposes of illustrating a clear example, FIG. 5is described in connection with certain elements of FIG. 1. However,other embodiments of FIG. 5 may be practiced in many other contexts andreferences herein to units of FIG. 1 are merely examples that are notintended to limit the broader scope of FIG. 5.

3.1 Receiving Data

At block 502, crop seed data and weather data related to a field arereceived by the agricultural intelligence computer system 130. Forexample, the agricultural intelligence computer system 130 may receivefield data 106 from the field manager computing device 104 and externaldata 110 from the external data server 108. Field data 106 may include,but is not limited to, crop seed data that identifies which specifichybrid has been planted by the user 102 and geo-location informationrelated to the user's 102 field.

External data 110 received from the external data server 108 may includehistorical property information for a variety of hybrid seeds andhistorical geo-location specific weather data. In an embodiment,historical geo-location specific weather data may be used in determiningcoefficients used in calculating R6 and grain moisture dry down. In anembodiment, external data 110 may be available from one or moredifferent external servers 108. For example, external data 110 relatedto historical seed property information for a variety of hybrid seedsmay be available from the Farmer's Independent Research of SeedTechnologies (FIRST) data repository. FIRST is a collaborative seed testprogram that utilizes farmers and seed producers to track planting andharvest of a variety of hybrid seed products across multiple fields inseveral corn producing states in the United States. Hybrid seedmeasurements include corn yield and grain moisture at time of harvestfor each field and hybrid seed type. The historical measurements arecollected and stored in a publicly accessible database. In anotherembodiment, geo-location specific weather information may be availablefrom one or more different external servers 108 that specifically storehistorical weather information.

In an embodiment, the specific field data 106 received may be used todetermine which external data 110 is to be received by the agriculturalintelligence computer system 130. FIG. 6 depicts an embodiment of adetailed flow diagram for receiving field data 106 and external data110. At block 602, the communication layer 132 receives field data 106from the field manager computing device 104. The communication layer 132then relays the field data 106, which includes hybrid seed type andgeo-location information, to the harvest time application logic 171.

At block 604, the harvest time application logic 171 determines whetherexternal data 110 is needed from the external data server 108. Forexample, specific external data 110 related to the weather conditionsand properties of the specific hybrid seed type are required toaccurately predict optimal harvest times. In embodiment, the harvesttime application logic 171 may first query the model and field datarepository 160, to determine if and to what extent external data 110 isrequired. For example, if the model and field data repository 160 haspreviously stored weather and seed property data for the specific hybridseed, then the harvest time application logic 171 may not need any newexternal data 110. In another example, the harvest time applicationlogic 171 may query the model and field data repository 160 anddetermine that only external data 110 related to the previous week'sweather conditions is required from the external data server 108.

At block 606, if the harvest time application logic 171 has determinedexternal data 110 is required, then the harvest time application logic171 creates a query for an external data server 108.

At block 608, the harvest time application logic 171 requests, using thecreated query, external data 110 from the external data server 108. Atblock 610, the harvest time application logic 171 stores the receivedfield data 106 and external data 110 in the model and field datarepository 160.

If at block 604, the harvest time application logic 171 determines aquery for external data 110 is not required, then the received fielddata 106 is stored in the model and field data repository 160 (block610).

3.2 Equilibrium Moisture Content Logic

At block 504 in FIG. 5, the EMC logic 172 creates an EMC time seriesusing external data 110 stored in the model and field data repository160. The EMC logic 172 calculates dry-basis EMC values for each daybased on available daily weather data points from the stored externaldata 110. A dry-basis EMC value represents a percentage of the moisturecontent of a given sample divided by the dry mass of the given sample.

In an embodiment, the EMC logic 172 uses the external data 110including: temperature values related to daily maximum, minimum,average, and at dew point; a daily derived relative humidity fraction;and empirically derived grain dependent constants. The EMC logic 172determines the EMC value at a specific time using the Chung-Pfostequation:

M _(eq)(t)=E−F*ln[−(T _(avg)(t)+C)*ln RH(t)]

where M_(eq) (t) equals the average daily dry-basis EMC fraction at timet; T_(avg) (t) equals the average daily temperature at time t, inCelsius; RH(t) equals the average daily relative humidity fraction attime t; E, F, and C are Chung-Pfost equation constants specific to corn.

In an embodiment, relative humidity, RH(t), may be calculated using thefollowing equation:

${{RH}(t)} = \frac{P_{v}\left( {T_{dew}(t)} \right)}{0.5\left( {{P_{v}\left( {T_{\max}(t)} \right)} + {P_{v}\left( {T_{\min}(t)} \right)}} \right.}$

where T_(dew) (t), T_(max)(t), T_(min)(t) equal the dew point, maximum,and minimum temperatures at time t, in Celsius.

P_(v)(T) equals the saturated vapor pressure, in kPa, for a giventemperature T, where P_(v)(T) is calculated for a given temperatureusing the following equation:

P _(v)(T)=0.6108^((17.27T/(T+237.3)))

where 0.6108 equals a reference saturation vapor pressure, in kPa, at 0degrees Celsius, and 17.27 and 237.3 are constants used to determinepartial pressure of water vapor.

In other embodiments, relative humidity may be calculated using eitherModified-Oswin equation, Strohman-Yoerger equation, Modified-Halseyequation, Chen-Clayton equation, or Modified Henderson equation.

In an embodiment, the EMC logic 172 calculates the dry-basis EMC valuefor each day in the time series starting at an estimated R6 date andending at the last date for which weather data is provided. In anembodiment, the estimated R6 date for the specific to the hybrid seed isincluded in the external data 110 stored in the model and field datarepository 160. In an embodiment, the EMC logic 172 sends the EMC timeseries to the harvest time application logic 171 for storage in themodel and field data repository 160.

3.3 Grain Moisture Logic

At block 506, the grain moisture logic 173 calculates the grain moisturecontent at R6 based upon the relative maturity of the specific hybridseed, a starting moisture coefficient, an adjustment coefficient, andthe average relative maturity of corn seeds. The purpose calculating aspecific hybrid seed moisture content at R6 is that it provides a grainmoisture starting point for determining the grain moisture dry down rateand optimal harvest time.

In an embodiment, the grain moisture logic 173 determines the start dateof R6 based upon external data 110, where the external data 110 includesan estimated R6 date for the specific hybrid seed. The estimated R6 datemay be based upon a phenology database of observed lifecycles of hybridcorn varieties. In another embodiment, R6 start date may be retrievedfrom an internal phenology database maintained in the model and fielddata repository 160.

In an embodiment, the grain moisture logic 173 is programmed to use thefollowing parameters to determine specific hybrid seed grain moisture atR6:

-   -   1) A estimated distribution of grain moisture content for corn        seed varieties.    -   2) A estimated distribution of an R6 adjustment factor based        upon observed relative maturity adjustments from corn seed        varieties.    -   3) Estimated relative maturity for the specific hybrid in        question.    -   4) An average relative maturity for the set of corn seed        varieties observed.

FIG. 7 depicts an embodiment by which the grain moisture logic 173estimates grain moisture content for the specific hybrid seed at R6,using the parameters above. At block 702, the grain moisture logic 173creates a posterior distribution of grain moisture content using theexternal data 110 stored in the model and field data repository 160. Forexample, the external data 110 may include measured and/or calculatedmoisture data at R6 for each of the hybrid seed varieties. In anembodiment, the grain moisture logic 173 may use Gibbs sampling methodfor sampling observations into a probability distribution that becomesthe posterior distribution of grain moisture at R6. The Gibbs samplingmethod is a Markov chain Monte Carlo method for obtaining a sequence ofrandom samples from a probability distribution when direct sampling isotherwise difficult. Other embodiments may implement different Markovchain Monte Carlo methods, such as Hamiltonian Monte Carlo or theMetropolis-Hastings algorithm. In yet other embodiments simulations ofthe grain dry down process may be performed with a range of possibleparametric values to take into account for their uncertainties. In anembodiment, the grain moisture logic 173 uses the mean value of theposterior distribution of grain moisture at R6 as the starting grainmoisture at R6. In an alternative embodiment, the grain moisture logic173 may use the median value of the posterior distribution of grainmoisture at R6 as the starting grain moisture at R6. In yet anotherembodiment, the grain moisture logic 173 uses the entire posteriordistribution dataset of grain moisture at R6 to create a set of R6 grainmoisture values to be evaluated.

At block 704, the grain moisture logic 173 creates a posteriordistribution of an R6 adjustment factor, where the R6 adjustment factoris a calculated value for how much the relative maturity of each hybridseed variety needs to be adjusted based upon the observed grain moistureat harvest. For example, the grain moisture logic 173 may calculate theobserved relative maturity of each hybrid seed sample and then determinehow much the estimated relative maturity would need to be adjusted inorder to align with the observed value. In an embodiment, the grainmoisture logic 173 may use Markov chain Monte Carlo methods for samplingthe observed adjustment factor into a posterior distribution for arelative maturity adjustment coefficient. In an embodiment, the grainmoisture logic 173 uses the median value of the posterior distributionfor the relative maturity adjustment coefficient as the relativematurity adjustment coefficient. In an alternative embodiment, the grainmoisture logic 173 may use the mean value of the posterior distributionfor the relative maturity adjustment coefficient as the relativematurity adjustment coefficient.

At block 706, the grain moisture logic 173 sets a baseline relativematurity as the average relative maturity for all observed corn seedvarieties. The baseline relative maturity value is used for determininghow much to adjust the moisture of a given hybrid seed based upon thedifference of the given hybrid seed's relative maturity to the baselinerelative maturity. For example, the baseline relative maturity may beset as the average relative maturity of all hybrid seed varieties.

At block 708, the grain moisture logic 173 calculates the hybrid seedgrain moisture at R6 as a function of the hybrid seed's relativematurity versus the baseline relative maturity of all observed hybridseed varieties. In an embodiment, the grain moisture logic 173 uses thefollowing equation to determine hybrid seed grain moisture at R6:

M(t=R6)=a+b*(rm _(hybrid) −rm _(baseline))

where M(t=R6) equals the hybrid seed wet-basis moisture content at R6; aequals the posterior median of grain moisture content at R6 for all cornseed varieties; b equals the posterior median of the relative maturityadjustment coefficient for all corn seed varieties; rm_(hybrid) equalsthe relative maturity of the hybrid seed; rm_(baseline) equals theaverage relative maturity of all corn seed varieties.

In an embodiment, the grain moisture logic 173 sends the calculated thegrain moisture content at R6 of the hybrid seed to the harvest timeapplication logic 171 to be stored in the model and field datarepository 160.

3.4 Grain Dry Down Logic

At block 508, the grain dry down logic 174 creates a grain dry down timeseries for the specific hybrid seed. The grain dry down time series is aset of dry down rates and hybrid seed moisture content corresponding toa specific day during the dry down process. Grain dry down refers to theexchange of moisture from the hybrid seed kernel to the outside air.

In an embodiment, external data 110 used to calculate daily grain drydown includes: the kernel moisture value of the specific hybrid seed atR6 as calculated by the grain moisture logic 173 as M(t=R6), themoisture content of the ambient air at specific dates as provided by theEMC time series, and historical dry down data for hybrid seed varieties.In an embodiment, the grain dry down logic 174 calculates the rate ofdaily grain dry down as the difference between the kernel moisture andthe moisture of ambient air at that specific date, multiplied by adrying coefficient.

In an embodiment, the grain dry down logic 174 calculates the grain drydown rate starting from the R6 date of the specific hybrid seed. In anembodiment, the daily dry down rate is calculated using the followingequation:

$\frac{dM}{dt} = {{- \frac{k}{\left( {{rm}_{hybrid}/100} \right)^{p}}}*\left( {{M(t)} - {{EMC}(t)}} \right)}$

where

$\frac{dM}{dt}$

equals the cry Gown rate at time t, where t represents a specific dayand

$\frac{k}{\left( {{rm}_{hybrid}/100} \right)^{p}}$

equals the drying coefficient; M(t) equals the grain moisture content ofthe specific hybrid seed at time t; EMC (t) equals the equilibriummoisture content of the ambient air at time t.

In an embodiment, the drying coefficient is calculated as a function ofthe relative maturity of the specific hybrid seed and derived parametersfrom historical moisture data collected from corn seed varieties. In anembodiment, parameters k and p describe how drying coefficients shouldbe adjusted according to relative maturity. In another embodiment,multiple parameters, along with parameters k and p, may be used todescribe the relationship between dry down and relative maturity. Theseparameters may describe posterior distributions created from historicalmoisture data collected from corn seed varieties, where parameters k andp are the respective posterior medians from the posterior distributions.For example, if the posterior medians k=0.030, p=3.6, and the relativematurity of the specific hybrid seed is 95, then the drying coefficientfor hybrid seed₉₅ would equal:

$\frac{0.03}{\left( {95/100} \right)^{3.6}} \approx {0.036{per}{day}}$

However using the same posterior median parameters, if the hybrid seedrelative maturity is 115, then the drying coefficient for hybrid seed₁₁₅would equal:

$\frac{0.03}{\left( {115/100} \right)^{3.6}} \approx {0.018{per}{day}}$

Therefore the drying coefficient is dependent on the relative maturitybecause hybrid seeds with shorter relative maturity tend to dry at ahigh drying coefficient than hybrid seeds with longer relative maturity.

FIG. 8 depicts an example of calculating the daily dry down ratestarting at R6 (dry down start date). At block 802, the grain dry downlogic 174 identifies the parameters for determining the daily dry downrate starting at R6. For example, when t=R6 the grain dry down logic 174uses the calculated hybrid seed moisture content from the grain moisturelogic 173 as M(t=R6) and looks up from the EMC time series the value forEMC(t=R6). In an embodiment, the grain dry down logic 174 calculates adry down start date drying coefficient parameter using the relativematurity for the specific hybrid seed and posterior medians forcoefficients k and p. In another embodiment, the grain dry down logic174 may retrieve a previously stored drying coefficient provided thatthe relative maturity of the hybrid seed and posterior medians ofcoefficients k and p have not changed.

At block 804, the grain dry down logic 174 calculates the daily dry downrate at time t using the daily dry down rate equation described above.For example, if the parameters are:

M(t = R6) = 28% EMC(t = R6) = 12% k = 0.03 p = 3.6 rm = 95${{drying}{coefficient}} = {\frac{k}{\left( {{rm}_{hybrid}/100} \right)^{p}} \approx \frac{0.03}{\left( {95/100} \right)^{3.6}} \approx 0.036}$

Then the dry down rate is calculated as follows:

$\frac{dM}{dt} = {{{- \frac{k}{\left( {{rm}_{hybrid}/100} \right)^{p}}}*\left( {{M(t)} - {{EMC}(t)}} \right)} = {{{- 0.036}*\left( {28 - 12} \right)} \approx {{- 0.576}\%}}}$

where

$\frac{dM}{dt}$

equals a drying rate of 0.576% of wet-basis moisture per day.

At block 806, the grain dry down logic 174 adds the calculated dry downrate at time t (block 804) and the hybrid seed moisture content M(t) tothe grain dry down time series. The grain dry down logic 174 thendetermines whether there are more data points available to calculatedadditional daily dry down rates. In an embodiment, the grain dry downlogic 174 calculates daily dry down rates for each data point within theEMC time series because the EMC time series represents each measured dayup until the latest measured day. By calculating the latest data, thegrain dry down time series will reflect the most accurate informationfor calculating current moisture levels.

If at block 806, the grain dry down logic 174 determines that there aremore data points then the grain dry down logic 174 proceeds to block802, where time t equals “t+1”. In an embodiment, at block 802 the graindry down logic calculates the hybrid seed moisture content M(t+1) bytaking M(t) and subtracting the calculated dry down rate,

$\frac{dM}{dt},$

at time t. For example, the hybrid seed moisture content at t=R6+1 is:

${M\left( {{R6} + 1} \right)} = {{{M\left( {R6} \right)} + {\frac{dM}{dt}\left( {t = {R6}} \right)}} = {{{28\%} + \left( {{- 0.576}\%} \right)} \approx {27.424\%}}}$

In an embodiment, the grain dry down logic 174 looks up from the EMCtime series the value for EMC(t=R6+1) and uses the same dryingcoefficient previously used. Then the grain dry down logic 174 proceedsto block 804 to calculate the dry down rate at R6+1.

In an embodiment, if at block 806 the grain dry down logic 174determines there are no more data points available to calculate the drydown rate, then the grain dry down logic 174 returns the grain dry downtime series to the harvest time application logic 171 to be stored inthe model and field data repository 160.

3.5 Harvest Recommendation Logic

At block 510, the harvest recommendation logic 175 determines optimalharvest time based upon the grain dry down time series and a desiredmoisture level. In an embodiment, the harvest time application logic 171sends the grain dry down time series and an optimal grain moisture valueto the harvest recommendation logic 175. An optimal grain moisture valueis a configurable wet-basis moisture value for grain based upon thedesired moisture content of the user 102. In an embodiment, the user 102may specify the optimal grain moisture value as part of the receivedfield data 106 from the field manager computing device 104. In anotherembodiment, the optimal moisture content may be preconfigured based upondesired grain moisture values of buyers of corn grain.

In an embodiment, the harvest recommendation logic 175 graphs the hybridseed moisture content values in the grain dry down time series andextrapolates future moisture content values based upon a trend line. Inan embodiment, extrapolation of future moisture content may be basedupon forecasted weather data that is used to calculate EMC values andthe changing rate of grain dry down, where the rate of grain dry downsteadily decreases as the moisture content value nears the EMC value. Inanother embodiment, the harvest recommendation logic 175 may extrapolatemoisture content values based upon historical EMC data values for thetime of year and geo-location and EMC values based on forecasted weatherdata.

In an embodiment, the harvest recommendation logic 175 returns arecommendation data model to the harvest time application logic 171. Therecommendation data model includes, but is not limited to, anextrapolated graph of the moisture content values of the hybrid seed,including predicted values, and a recommended harvest date that is basedupon the desired moisture content value of the hybrid seed. By providingboth the recommendation date and the moisture content dry down graph,the user 102 may better understand the grain dry down trend of his crop.

FIG. 9 depicts a sample recommendation data model, where graph 902depicts a the recommendation data model for field X. Line 904 is theextrapolated trend line based upon calculated moisture content valuesfrom the grain dry down time series. Point 906 is the predicted date(day R6+50) where the moisture content of the hybrid seed reaches 15%wet-basis moisture.

In an embodiment, the harvest time application logic 171 relays therecommendation data model to the presentation layer 134. Thepresentation layer 134 packages and sends the recommendation data modelin a format that is displayable on the field manager computing device104. In other embodiments, the harvest time application logic 171 storesthe recommendation data model in memory. Stored recommendation datamodels may later be used to improve methods used by the harvestrecommendation logic 175 and may be used for cross-validating futurerecommendation data models.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

1. A method for obtaining and displaying a harvest time recommendationfor crops grown on one or more agricultural fields comprising: receivingdata for an identification graphical user interface (GUI) over a digitaldata communication network from an agricultural intelligence computersystem; displaying the identification GUI on a user device, theidentification GUI providing an interface region for a user to inputidentification data for the one or more agricultural fields; afterreceiving input identification data from the user, transmitting the userinputted identification data to the agricultural intelligence computersystem; receiving data for a data manager GUI over the digital datacommunication network from the agricultural intelligence computersystem; displaying the data manager GUI on the user device, the datamanager GUI providing one or more regions for a user to input field datacomprising a plurality of values representing crop seed data for the oneor more agricultural fields identified; after receiving input field datafrom the user, transmitting the inputted field data to the agriculturalintelligence computer system; receiving data for a recommendation GUIover the digital data communication network from the agriculturalintelligence computer system; and displaying the recommendation GUI, therecommendation GUI displaying a harvest time recommendation forharvesting crop grown from a specific hybrid seed planted in the one ormore agricultural fields, based, at least in part, on the inputted fielddata and the inputted identification data.
 2. The method of claim 1,wherein the crop seed data includes digital data representing estimatedrelative maturity of the specific hybrid seed and R6 data for thespecific hybrid seed that is based upon historical phenology modelingdata.
 3. The method of claim 1, wherein the data manager GUI furtherincludes a weather input region for a user to input weather data and theharvest time recommendation is based, at least in part, on the inputtedweather data, and wherein the weather data comprises digital datarepresenting values for historical average, maximum, and minimum dailytemperature, historical daily dewpoint temperatures, historical averagerelative humidity, and historical saturated vapor pressure for a giventemperature for the one or more fields.
 4. The method of claim 1,wherein the harvest time recommendation is based on a grain dry downtime series determined by the agricultural intelligence computer systemthat represents moisture levels of the specific hybrid seed, and thegrain dry down time series is based, at least, in part, on creation ofan equilibrium moisture content time series by the agriculturalintelligence computer system which comprises: deriving an average dailydry-basis equilibrium moisture content fraction value at a specific timeusing computer execution of a digital representation of a Chung-Pfostequation; compiling the equilibrium moisture content time series usingderived average daily dry-basis equilibrium moisture content fractionvalues over a series of time data points.
 5. The method of claim 4,wherein the specific hybrid seed is a type of hybrid seed and the graindry down time series is based, at least in part, on an R6 moisturecontent calculated by the agricultural intelligence computer system, andwherein calculation of the R6 moisture content comprises: deriving a drydown start date drying coefficient based, at least in part, on R6 datafor the specific hybrid seed; deriving an R6 adjustment factor based, atleast in part, on relative maturity of the specific hybrid seed;calculating an R6 moisture content for the specific hybrid seed usingthe dry down start date drying coefficient, the R6 adjustment factor,the relative maturity of the specific hybrid seed, and a baselinerelative maturity of the type of hybrid seed.
 6. The method of claim 5,wherein the dry down start date drying coefficient is calculated as amedian of a posterior distribution of R6 dates for the specific hybridseed, where the posterior distribution of R6 dates is a compilation ofhistorical R6 dates for the specific hybrid seed measured across one ormore fields.
 7. The method of claim 5, wherein the R6 adjustment factoris calculated as a median of a posterior distribution of variationbetween observed maturity dates and estimated R6 dates for the specifichybrid seed measured across one or more fields.
 8. The method of claim5, wherein the baseline maturity duration timeframe is configured basedupon the type of the hybrid seed.
 9. The method of claim 5, whereincreation of the grain dry down time series comprises: calculating a rateof change in moisture value for the specific hybrid seed at a specifictime, where the rate of change in moisture equals a difference betweenthe moisture content within the specific hybrid seed and the equilibriummoisture content at a specific time, multiplied by a drying coefficient;determining the moisture content within the specific hybrid seed basedon the R6 moisture content for the specific hybrid seed; deriving theequilibrium moisture content from the equilibrium moisture content timeseries at the specific time calculated; determining the dryingcoefficient based upon a function of relative maturity expressed indays; compiling the calculated rate of change in moisture values tocreate the grain dry down time series.
 10. The method of claim 4,wherein the harvest recommendation is a date from the grain dry downtime series where the grain moisture equals a target moisture value. 11.One or more non-transitory storage media storing instructions which,when executed by one or more computing devices, cause performance of amethod comprising the steps of: receiving data for an identificationgraphical user interface (GUI) over a digital data communication networkfrom an agricultural intelligence computer; displaying theidentification GUI on a user device, the identification GUI providing aninterface region for a user to input identification data for one or moreagricultural fields; after receiving input identification data from theuser, transmitting the inputted identification data to the agriculturalintelligence computer system; receiving data for a data manager GUI overa digital data communication network from the agricultural intelligencecomputer system; displaying the data manager GUI on the user device, thedata manager GUI providing one or more regions for a user to input fielddata comprising a plurality of values representing crop seed data forthe one or more agricultural fields identified; after receiving inputfield data from the user, transmitting the inputted field data to theagricultural intelligence computer system; receiving data for arecommendation GUI over a digital data communication network from theagricultural intelligence computer system; and displaying therecommendation GUI, the recommendation GUI displaying a harvest timerecommendation for harvesting crop grown from a specific hybrid seedplanted in the one or more agricultural fields, based, at least in part,on the inputted field data and the inputted identification data.
 12. Theone or more non-transitory storage media of claim 11, wherein the cropseed data includes digital data representing estimated relative maturityof the specific hybrid seed and R6 data for the specific hybrid seedthat is based upon historical phenology modeling data.
 13. The one ormore non-transitory storage media of claim 11, wherein the data managerGUI further includes a weather input region for a user to input weatherdata and the harvest time recommendation is based, at least in part, onthe inputted weather data, and wherein the weather data comprisesdigital data representing values for historical average, maximum, andminimum daily temperature, historical daily dewpoint temperatures,historical average relative humidity, and historical saturated vaporpressure for a given temperature for the one or more fields.
 14. The oneor more non-transitory storage media of claim 11, wherein the harvesttime recommendation is based on a grain dry down time series determinedby the agricultural intelligence computer system that representsmoisture levels of the specific hybrid seed, and the grain dry downtimer series is based, at least in part, on creation of an equilibriummoisture content time series by the agricultural intelligence computersystem which comprises: deriving an average daily dry-basis equilibriummoisture content fraction value at a specific time using computerexecution of a digital representation of a Chung-Pfost equation;compiling the equilibrium moisture content time series using derivedaverage daily dry-basis equilibrium moisture content fraction valuesover a series of time data points.
 15. The one or more non-transitorystorage media of claim 14, wherein the specific hybrid seed is a type ofhybrid seed and the grain dry down time series is based, at least inpart, on an R6 moisture content calculated by the agriculturalintelligence computer system, and wherein calculation of the R6 moisturecontent comprises: deriving a dry down start date drying coefficientbased, at least in part, on R6 data for the specific hybrid seed;deriving an R6 adjustment factor based, at least in part, on relativematurity of the specific hybrid seed; calculating an R6 moisture contentfor the specific hybrid seed using the dry down start date dryingcoefficient, the R6 adjustment factor, the relative maturity of thespecific hybrid seed, and a baseline relative maturity of the type ofhybrid seed.
 16. The one or more non-transitory storage media of claim15, wherein the dry down start date drying coefficient is calculated asa median of a posterior distribution of R6 dates for the specific hybridseed, where the posterior distribution of R6 dates is a compilation ofhistorical R6 dates for the specific hybrid seed measured across one ormore fields.
 17. The one or more non-transitory storage media of claim15, wherein the R6 adjustment factor is calculated as a median of aposterior distribution of variation between observed maturity dates andestimated R6 dates for the specific hybrid seed measured across one ormore fields.
 18. The one or more non-transitory storage media of claim15, wherein the baseline maturity duration timeframe is configured basedupon the type of the hybrid seed.
 19. The one or more non-transitorystorage media of claim 15, wherein creation of the grain dry down timeseries comprises: calculating a rate of change in moisture value for thespecific hybrid seed at a specific time, where the rate of change inmoisture equals a difference between the moisture content within thespecific hybrid seed and the equilibrium moisture content at a specifictime, multiplied by a drying coefficient; determining the moisturecontent within the specific hybrid seed based on the R6 moisture contentfor the specific hybrid seed; deriving the equilibrium moisture contentfrom the equilibrium moisture content time series at the specific timecalculated; determining the drying coefficient based upon a function ofrelative maturity expressed in days; compiling the calculated rate ofchange in moisture values to create the grain dry down time series. 20.The one or more non-transitory storage media of claim 14, whereindetermining the harvest recommendation is based upon selecting a datefrom the grain dry down time series where the grain moisture equals atarget moisture value.
 21. The method of claim 1, wherein displaying theharvest time recommendation includes displaying a moisture content drydown graph for the one or more fields.
 22. The method of claim 1,wherein the interface region of the identification GUI includes a mapand inputting identification data for the one or more fields includesthe user selecting specific regions graphically displayed on the map.23. The method of claim 1, wherein the interface region of theidentification GUI includes a map and inputting identification data forthe one or more fields includes the user drawing boundaries on the map.24. The method of claim 1, wherein the interface region of theidentification GUI includes selectable field information data from anexternal database and inputting identification data for the one or morefields includes the user selecting field information data correspondingto the one or more fields.
 25. The one or more non-transitory storagemedia of claim 11, wherein displaying the harvest time recommendationincludes displaying a moisture content dry down graph for the one ormore fields.
 26. The one or more non-transitory storage media of claim11, wherein the interface region of the identification GUI includes amap and inputting identification data for the one or more fieldsincludes the user selecting specific regions graphically displayed onthe map.
 27. The one or more non-transitory storage media of claim 11,wherein the interface region of the identification GUI includes a mapand inputting identification data for the one or more fields includesthe user drawing boundaries on the map.
 28. The one or morenon-transitory storage media of claim 11, wherein the interface regionof the identification GUI includes selectable field information datafrom an external database and inputting identification data for the oneor more fields includes the user selecting field information datacorresponding to the one or more fields.